The jackknife-after-bootstrap (JaB) method has been proposed for detecting influential observations in linear regression models. The performance of JaB and the traditional methods have been compared for four different influence measures by designed simulation study and real world examples. Design includes different sample sizes and various modeling scenarios. The results reveal that proposed method is a good competitor or even better than traditional methods
This thesis contributes to influence analysis in nonlinear regression and in particular the detectio...
Since its introduction by Efron [1], the bootstrap has been the object of research in statistics. We...
We propose two novel diagnostic measures for the detection of influential observations for regressio...
In this study, we adapt sufficient bootstrap into the jackknife-after-bootstrap (JaB) algorithm. The...
We propose a bootstrap approach to gauging the size of regression influence measures. The bootstrap ...
North-West University, Potchefstroom CampusMSc (Mathematical Statistics), North-West University, Pot...
In this paper, we adapt sufficient and ordered non-overlapping block bootsrap methods into jackknife...
Abstract: In this paper, the hierarchical ways for building a regression model by using bootstrap an...
A computer-intensive method for estimating small sample statistics and obtaining confidence interval...
AbstractThis paper studies how to identify influential observations in the functional linear model i...
This paper studies how to identify influential observations in the functional linear model in which ...
AbstractB. Efron introducedjackknife-after-bootstrapas a computationally efficient method for estima...
Influential observations (IO) are those observations that are responsible for misleading conclusions...
When using linear models, a common practice is to find the single best model fit used in predictions...
An influential observation is any point that has a huge effect on the coefficients of a regression l...
This thesis contributes to influence analysis in nonlinear regression and in particular the detectio...
Since its introduction by Efron [1], the bootstrap has been the object of research in statistics. We...
We propose two novel diagnostic measures for the detection of influential observations for regressio...
In this study, we adapt sufficient bootstrap into the jackknife-after-bootstrap (JaB) algorithm. The...
We propose a bootstrap approach to gauging the size of regression influence measures. The bootstrap ...
North-West University, Potchefstroom CampusMSc (Mathematical Statistics), North-West University, Pot...
In this paper, we adapt sufficient and ordered non-overlapping block bootsrap methods into jackknife...
Abstract: In this paper, the hierarchical ways for building a regression model by using bootstrap an...
A computer-intensive method for estimating small sample statistics and obtaining confidence interval...
AbstractThis paper studies how to identify influential observations in the functional linear model i...
This paper studies how to identify influential observations in the functional linear model in which ...
AbstractB. Efron introducedjackknife-after-bootstrapas a computationally efficient method for estima...
Influential observations (IO) are those observations that are responsible for misleading conclusions...
When using linear models, a common practice is to find the single best model fit used in predictions...
An influential observation is any point that has a huge effect on the coefficients of a regression l...
This thesis contributes to influence analysis in nonlinear regression and in particular the detectio...
Since its introduction by Efron [1], the bootstrap has been the object of research in statistics. We...
We propose two novel diagnostic measures for the detection of influential observations for regressio...